This paper proposes a simple method for guiding researchers in developing quantitative models of economic fluctuations. We show that a large class of models, including models with various frictions, are equivalent to a prototype growth model with time varying wedges that, at least on face value, look like time-varying productivity, labor taxes, and capital income taxes. We label the time varying wedges as efficiency wedges, labor wedges, and investment wedges. We use data to measure these wedges and then feed them back into the prototype growth model. We then assess the fraction of fluctuations accounted for by these wedges during the great depressions of the 1930s in the United States, Germany, and Canada. We find that the efficiency and labor wedges in combination account for essentially all of the declines and subsequent recoveries. Investment wedge plays at best a minor role.

We show that some classes of sterilized interventions have no effect on equilibrium prices or quantities. The proof does not depend on complete markets, infinitely-lived agents, Ricardian equivalence, monetary neutrality, or the law of one price. Moreover, regressions of exchange rates or interest differentials on variables measuring the currency composition of the debt may contain no information, in our theoretical economy, about the effectiveness of such interventions. Another class of interventions requires simultaneous changes in monetary and fiscal policy; their effects depend, generally, on the influence of tax distortions, government spending, and money supplies on economic behavior. We suggest that in applying the portfolio balance approach to the study of intervention, lack 01 explicit modeling of these features is a serious flaw.

This paper develops a forecasting procedure based on a Bayesian method for estimating vector autoregressions. The procedure is applied to ten macroeconomic variables and is shown to improve out-of-sample forecasts relative to univariate equations. Although cross-variables responses are damped by the prior, considerable interaction among the variables is shown to be captured by the estimates. We provide unconditional forecasts as of 1982:12 and 1963:3* We also describe how a model such as this can be used to make conditional projections and to analyse policy alternatives. As an example, we analyze a Congressional Budget Office forecast made in 1982:12. While no automatic causal interpretations arise from models like ours, they provide a detailed characterization of the dynamic statistical interdependence of a set of economic variables, which may help in evaluating causal hypotheses, without containing any such hypotheses themselves.